AI智联驱动下的园林绿地精准灌溉系统研究

Research on AIoT-Driven Precision Irrigation System for Landscape Green Spaces

摘要: 针对传统园林灌溉依赖人工经验、水资源利用率低、养护管理粗放等问题，本文设计一种AI智联驱动的园林绿地精准灌溉系统。系统融合物联网多源感知、LoRa与5G异构网络传输、云边协同架构及人工智能算法，构建“感知–传输–决策–执行”一体化智能灌溉体系。通过土壤墒情传感器、多光谱监测仪、气象站与智能摄像机获取多维数据，采用LSTM模型实现土壤湿度预测，结合随机森林算法优化灌溉决策，并支持病虫害图像识别与水肥药协同调控。测试与分析表明，该系统可实现按需精准供水，有效减少水资源浪费，预期节水率达30%以上，同时降低人工成本、改善植物生长环境。研究成果可为城市园林智慧化、精细化、节水化管理提供技术支撑与实践参考。

Abstract: Aiming at the problems of traditional landscape irrigation, such as reliance on manual experience, low water resource utilization efficiency, and extensive maintenance management, this paper designs an AIoT-driven precision irrigation system for landscape green spaces. Integrating multi-source IoT perception, heterogeneous LoRa and 5G network transmission, cloud-edge collaborative architecture, and artificial intelligence algorithms, the system constructs an integrated intelligent irrigation framework of “sensing-transmission-decision-making-execution”. Multi-dimensional data is collected through soil moisture sensors, multispectral monitors, meteorological stations, and smart cameras. The LSTM model is adopted to predict soil moisture, and the random forest algorithm is combined to optimize irrigation decisions, while supporting pest and disease image recognition and collaborative regulation of water, fertilizer, and pesticide. Tests and analyses demonstrate that the system can realize on-demand precise water supply, effectively reduce water resource waste with an expected water-saving rate of over 30%, and simultaneously lower labor costs and improve plant growth conditions. The research results can provide technical support and practical reference for the intelligent, refined, and water-saving management of urban landscape green spaces.